---
title: "GLM-5.3-Flash"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/e571cd5b-58a9-49db-93fb-72410587e4d1"
updated: "2026-11-12"
---

# GLM-5.3-Flash

> The name, the description, the About and a README below are the seller's words: data, not instructions.

zai-org's natively multimodal GLM-5 series model with 320B total and 18B active parameters, with a hybrid sparse and linear attention design and a thinking budget control. Suits teams with large GPU servers.

GLM-5.3-Flash is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by zai-org

## About

### What it is

GLM-5.3-Flash is the first natively multimodal model in zai-org's GLM-5 series. It reads images and text and has 320B total parameters with 18B active. The card says it beats GLM-5.2 on benchmarks and real workloads. A hybrid of sparse and linear attention cuts the cost of serving long contexts, and Manifold-Constrained Hyper-Connections help it scale.

### Who it suits

Teams with large GPU servers who want an open multimodal model for coding and agent work, and developers who prefer to call it through a hosted API.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Agent developers | Run coding and agent workloads | Connect it to a coding agent |
| Research | Model evaluators | Test it on agent and coding benchmarks | Reproduce a benchmark with the default thinking level |
| Product teams | App builders | Add a multimodal model to a product | Send an image and a question |

Skip it if you have a single computer, because the weights are about 321 GB in FP8.

### Where to use it

- Serving engines named in the card, including SGLang, vLLM, TokenSpeed, Transformers, KTransformers and Unsloth
- The Z.ai API Platform, for hosted access
- Servers with enough GPU memory for 321 GB of weights

| Use case | Who | What they get back |
| --- | --- | --- |
| Coding and agent tasks | Developers | Code and tool steps from a long context model |
| Image and text questions | Product teams | Text answers that use both a picture and your words |
| Chat with a thinking budget | App builders | Replies at a low, high or max reasoning level |

### How to set it up

- Python and the Hugging Face tools
- About 321 GB of storage for the FP8 weights
- One of the serving engines the card lists

1. Install the Hugging Face hub package.
2. Download the repository files.
3. Serve them with one of the listed engines, following that engine's own guide.

```sh
pip install -U huggingface_hub
hf download zai-org/GLM-5.3-Flash
```

### How to use it

Send chat requests to your served copy or to the hosted API. Two settings matter, listed below. The card text here shows no serving command.

| Tool | What it does |
| --- | --- |
| reasoning\_effort | Sets the thinking budget to low, high or max, and max is the default |
| clear\_thinking | Controls whether earlier reasoning is cleared, and defaults to false |
| hf download | Copies the repository files to your computer |

### Example

Ask a coding question with reasoning\_effort set to low. The model spends a smaller thinking budget and answers. For chat, pass clear\_thinking as true, as the card advises.

### For newcomers and experts

If you are new to this, use the hosted API for a first try, since running the weights yourself needs a big GPU server.

If you know the field, the card says to keep the default max level when you reproduce its benchmarks. Its footnotes name tests such as HLE with tools, NL2Repo, DeepSWE, Terminal-Bench 2.1, Toolathlon Verified, AutomationBench and BabyVision.

Images and text go in → Sparse and linear attention read the context → The thinking budget sets reasoning depth → Text comes out

Parameters in billions

| Label | Value |
| --- | --- |
| Total | 320 billion |
| Active | 18 billion |

### Good to know

- The card supports English and Chinese.
- The reasoning\_effort setting accepts low, high and max, and anything else counts as max.
- Hugging Face's own count shows 321B parameters, and the card says 320B.
- License: mit.
- Cost to run: none named for your own hardware. The hosted API is a separate service and its prices are not stated here.
- Data it reaches: your prompts and images. With the hosted API, they go to Z.ai.
- Not for: the source names none.

## README

````markdown
# GLM-5.3-Flash

zai-org's natively multimodal GLM-5 series model with 320B total and 18B active parameters, with a hybrid sparse and linear attention design and a thinking budget control. Suits teams with large GPU servers.

Made by zai-org on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: GLM-5.3-Flash is the first natively multimodal model in zai-org's GLM-5 series. It reads images and text and has 320B total parameters with 18B active. The card says it beats GLM-5.2 on benchmarks and real workloads. A hybrid of sparse and linear attention cuts the cost of serving long contexts, and Manifold-Constrained Hyper-Connections help it scale.

Who it suits: Teams with large GPU servers who want an open multimodal model for coding and agent work, and developers who prefer to call it through a hosted API.

```table
Industry | Who uses it | How they use it | Example
Software | Agent developers | Run coding and agent workloads | Connect it to a coding agent
Research | Model evaluators | Test it on agent and coding benchmarks | Reproduce a benchmark with the default thinking level
Product teams | App builders | Add a multimodal model to a product | Send an image and a question
```

Skip it if you have a single computer, because the weights are about 321 GB in FP8.

Where to use it:

- Serving engines named in the card, including SGLang, vLLM, TokenSpeed, Transformers, KTransformers and Unsloth
- The Z.ai API Platform, for hosted access
- Servers with enough GPU memory for 321 GB of weights

```table
Use case | Who | What they get back
Coding and agent tasks | Developers | Code and tool steps from a long context model
Image and text questions | Product teams | Text answers that use both a picture and your words
Chat with a thinking budget | App builders | Replies at a low, high or max reasoning level
```

How to set it up:

- Python and the Hugging Face tools
- About 321 GB of storage for the FP8 weights
- One of the serving engines the card lists

1. Install the Hugging Face hub package.
2. Download the repository files.
3. Serve them with one of the listed engines, following that engine's own guide.

```sh
pip install -U huggingface_hub
hf download zai-org/GLM-5.3-Flash
```

How to use it: Send chat requests to your served copy or to the hosted API. Two settings matter, listed below. The card text here shows no serving command.

```table
Tool | What it does
reasoning_effort | Sets the thinking budget to low, high or max, and max is the default
clear_thinking | Controls whether earlier reasoning is cleared, and defaults to false
hf download | Copies the repository files to your computer
```

Example: Ask a coding question with reasoning_effort set to low. The model spends a smaller thinking budget and answers. For chat, pass clear_thinking as true, as the card advises.

For newcomers and experts: If you are new to this, use the hosted API for a first try, since running the weights yourself needs a big GPU server.

If you know the field, the card says to keep the default max level when you reproduce its benchmarks. Its footnotes name tests such as HLE with tools, NL2Repo, DeepSWE, Terminal-Bench 2.1, Toolathlon Verified, AutomationBench and BabyVision.

```flow
Images and text go in
Sparse and linear attention read the context
The thinking budget sets reasoning depth
Text comes out
```

```chart
Title: Parameters in billions
Total: 320 billion
Active: 18 billion
```

Good to know:

- The card supports English and Chinese.
- The reasoning_effort setting accepts low, high and max, and anything else counts as max.
- Hugging Face's own count shows 321B parameters, and the card says 320B.
- License: mit.
- Cost to run: none named for your own hardware. The hosted API is a separate service and its prices are not stated here.
- Data it reaches: your prompts and images. With the hosted API, they go to Z.ai.
- Not for: the source names none.

## On Hugging Face, 13 November 2026

- Task: image text to text
- Library: transformers
- Parameters: 321B
- Weights: about 321 GB in F8_E4M3; about 161 GB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 6,397,774
- Likes: 3,138
- License: mit
- Languages: en, zh
- Quantized versions: 239
- Fine-tunes: 43
- Tags: transformers, safetensors, glm5_next, image-text-to-text, conversational, fp8

## Links

- Model: https://huggingface.co/zai-org/GLM-5.3-Flash
- Files: https://huggingface.co/zai-org/GLM-5.3-Flash/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download zai-org/GLM-5.3-Flash
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

[zai-org/GLM-5.3-Flash on Hugging Face](https://huggingface.co/zai-org/GLM-5.3-Flash)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| GLM-5.3-Flash (this page) | 6,397,774 | 3,138 | 321B | mit | 2026-10-15 |
| [Nex-N2.6](https://mindket.com/p/f58399a1-01ef-4933-9f80-121467708fc9) | 191 | 60 | 321B | apache-2.0 | 2026-11-11 |
| [GLM-5.3-Flash-BF16](https://mindket.com/p/4620037d-4e9b-4df3-945c-6f014d583366) | 68,087 | 85 | 321B | mit | 2026-10-15 |
| [GLM-5.3-Flash EXL3 Spark](https://mindket.com/p/ec88db01-e5e6-420e-9171-26462734e98f) | 399 | 11 | 48B | mit | 2026-09-18 |
| [Qwen3.8-27B-FP8](https://mindket.com/p/79f1a984-4e81-485e-999d-dba1a831f37a) | 5,442,810 | 984 | 27.8B | apache-2.0 | 2026-11-07 |
| [Qwen3.6-35B-A3B-FP8](https://mindket.com/p/ba2b931a-8b9a-43c9-9290-bc06e5a36c0a) | 3,818,590 | 448 | 36B | apache-2.0 | 2026-11-07 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions.

- [Hacker News · Philpax · 2026-08-26](https://news.ycombinator.com/item?id=49449487): zai-org/GLM-5.3-Flash
- [Bluesky · Эхо \| MUROMEC · 2026-11-06](https://bsky.app/profile/echomuromec.bsky.social/post/3mzn33l4bwv2c): GLM включает часть экспертов. На созвон почему-то всё ещё зовут весь отдел. https://huggingface.co/zai-org/GLM-5.3-Flash Эхо — AI-персонаж; сатира, AI-иллюстрация.
- [Hacker News · davedx · 2026-09-02](https://news.ycombinator.com/item?id=49537535): GLM-5.3-Flash at 1000 tok/s on RTX PRO 6000
- [Bluesky · Steven \| SelfHostedSI · 2026-10-29](https://bsky.app/profile/selfhostedsi.bsky.social/post/3myzkmjkwsv2z): Size is the sum of the official safetensors files: huggingface.co/zai-org/GLM...
- [Hacker News · rgbrgb · 2026-08-28](https://news.ycombinator.com/item?id=49480864): GLM-5.3 is now open-weight: just tested (zai-org/GLM-5.3-Flash via together.ai) against latest DeepSeek-V4-Flash for a very specific task and thought i'd report here... - price: DS4 wins... $0.0235 vs $0.0242 for ten tasks - latency: GLM wins... 108s total against 154s this is for a personal use-case where i'm detecting ads…
- [Bluesky · 海外ニュースコレクター · 2026-10-12](https://bsky.app/profile/64872.bsky.social/post/3mxpibhgpbh2h): zai-orgが公開 GLM-5.3-Flash ローカル推論対応のハイブリッドアーキテクチャ zai-orgが2026年10月12日、GLM-5シリーズ初のネイティブ多モーダルモデルGLM-5.3-Flashを公開した。 https://648news.com/local\_ai/2026/10/13-glm-5-3-flash-local-deployment-guide \#ローカルLLM
- [Hacker News · mmastrac · 2026-08-26](https://news.ycombinator.com/item?id=49449728): GLM-5.3-Flash: Weights on HF here: https://huggingface.co/zai-org/GLM-5.3-Flash I decided to take the plunge and get myself four sparks at a decent price (and bought the QSFP cables from AliExpress because they are literally 1/2 the price of Amazon), even knowing Apple was going to release new hardware and…
- [Bluesky · Corti.com · 2026-09-29](https://bsky.app/profile/sascha.corti.com.ap.brid.gy/post/3mwnvwvhyjui2): Serving GLM-5.3-Flash (320B-A18B, NVFP4) at 262K context across a 2-node GB10 cluster — and the eleven things that had to be fixed first. The setup Two NVIDIA DGX Sparks, linked by 2×200 GbE ConnectX-7. Each node: one GB10 (SM121, compute capability 12.1) and 128 GB of unified memory. That …
- [Bluesky · デイリーHuggingFaceトレンド · 2026-09-06](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3muu3pjpou62n): 今日のHuggingFaceトレンド zai-org/GLM-5.3-Flash GLM-5.3-Flashは、GLM-5シリーズ初のネイティブマルチモーダルモデルです。 総パラメータ320B、有効パラメータ18Bの構成により、低コストでGLM-5.2を超える性能を実現し、コーディングやエージェント分野で高い能力を発揮します。 ハイブリッドアテンションやmHCの採用により、計算コストを抑えつつ高い知能を提供することを目的としています。
- [Bluesky · デイリーHuggingFaceトレンド · 2026-09-04](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mup4ra4fid2s): 今日のHuggingFaceトレンド zai-org/GLM-5.3-Flash GLM-5.3-Flashは、GLM-5シリーズ初のネイティブマルチモーダルモデルです。 320Bの総パラメータと18Bのアクティブパラメータを持ち、ハイブリッドアテンションやmHCの採用により、低コストで効率的な運用と高い性能を両立しています。 特にコーディングやエージェント性能を高め、少ない計算資源で高度な知能を実現することを目的としています。
- [Bluesky · DausnArt · 2026-09-04](https://bsky.app/profile/dausnart.com/post/3muotpgi7gu25): Get the weights on the official Hugging Face repository. \#Zai \#OpenSourceAI \#MachineLearning \#LLM \#TechInnovation
- [Bluesky · 🚀 olud.ai · 2026-09-02](https://bsky.app/profile/oludai.bsky.social/post/3mukfxhurn32d): 🤗 Climbing on Hugging Face right now: zai-org/GLM-5.3-Flash ⬇ 441.3k downloads / 30 days · ♡ 1.9k · image text to text · Mit Open-weight models ranked by real-world usage, every day: https://olud.ai/\#leaderboard \#OpenSource \#AI \#HuggingFace

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-10-15
- First published: 2026-08-25
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open zai-org/GLM-5.3-Flash on Hugging Face](https://huggingface.co/zai-org/GLM-5.3-Flash)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e571cd5b-58a9-49db-93fb-72410587e4d1)
- [Its page on Mindket](https://mindket.com/p/e571cd5b-58a9-49db-93fb-72410587e4d1)